Date
Publisher
arXiv
Early detection of at-risk students and timely academic intervention pose major challenges in advanced mathematics education, where complex conceptual hierarchies and nonlinear learning trajectories often hold back students' academic performance. This study adopts multimodal data analytics to build a dynamic framework for learning behavior prediction and academic early warning. It constructs a hierarchical knowledge graph ontology, realizes adaptive edge weighting according to problem difficulty and student performance, and combines heterogeneous graph attention with temporal sequence modeling to capture students' evolving knowledge states. Empirical tests on semester-long multimodal datasets prove that this method can accurately identify high-risk students and effectively track error propagation. Targeted interventions greatly improve students' knowledge mastery and reduce academic risks. The results verify that integrating knowledge graph analytics with multimodal temporal modeling can deliver more efficient and personalized learning support for advanced mathematics education.
Study at a glance
- Question: Can a multimodal knowledge graph framework accurately predict student learning difficulties and guide targeted academic early warnings in mathematics?
- Setting and data: Undergraduate mathematics students taking an advanced mathematics course, focusing on eigenvalues and eigenvectors over one semester; includes a sample of 10 high-risk students.
- Method: System development and empirical evaluation combining dynamic knowledge graphs, graph attention networks, and sequence modeling (GAT-LSTM).
Why it matters
Early detection of at-risk students and timely intervention are critical in advanced mathematics, where complex conceptual hierarchies hinder success. However, existing methods often fail to capture dynamic knowledge changes and rely on unimodal data.
Key findings
- Across a sample of 10 high-risk students, average concept mastery probability increased from 0.43 before intervention to 0.71 afterward, an improvement of roughly 65%.
- Average transition difficulty between core concepts decreased from 0.38 to 0.21, and mastery of matrix diagonalization rose from 0.35 to 0.68.
- The risk utility function showed an average drop of 0.19 in the probability of failure propagation, with most students sustaining their progress four weeks post-intervention.
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